INTRODUCTORY STATISTICS


 Anabel Cummings
 3 years ago
 Views:
Transcription
1 INTRODUCTORY STATISTICS FIFTH EDITION Thomas H. Wonnacott University of Western Ontario Ronald J. Wonnacott University of Western Ontario WILEY JOHN WILEY & SONS New York Chichester Brisbane Toronto Singapore
2 CONTENTS PART I BASIC PROBABILITY AND STATISTICS 1 The Nature of Statistics 11 Random Sampling: A Political Poll 12 Randomized Experiments: Testing a Hospital Routine Observational Studies vs. Randomized Experiments Brief Outline of the Book 20 Chapter 1 Summary 20 2 Descriptive Statistics 21 Frequency Tables and Graphs Center of a Distribution Spread of a Distribution Statistics by Computer Linear Transformations Calculations Using Relative Frequencies The Use and Misuse of Graphs 53 Chapter 2 Summary 64 3 Probability 31 Introduction Probability Models Compound Events Conditional Probability Independence Bayes Theorem: Tree Reversal Other Views of Probability 99 Chapter 3 Summary Probability Distributions 41 Discrete Random Variables Mean and Variance The Binomial Distribution Continuous Distributions The Normal Distribution A Function of a Random Variable 134 *47 Expected Value in Bidding 141 Chapter 4 Summary Two Random Variables Distributions A Function of Two Random Variables Covariance Linear Combination of Two Random Variables 170 Chapter 5 Summary 176 Review Problems (Chapters 15) 182 PART II INFERENCE FOR MEANS AND PROPORTIONS Sampling Random Sampling Moments of the Sample Mean The Shape of the Sampling Distribution Proportions (Percentages) 207 *65 SmallPopulation Sampling 215 *66 Monte Carlo 218 Chapter 6 Summary Point Estimation Populations and Samples Efficiency of Unbiased Estimators Efficiency of Biased and Unbiased Estimators 239 *74 Consistent Estimators 244 Chapter 7 Summary 248 XIII
3 XIV CONTENTS 8 Confidence Intervals A Single Mean SmallSample t Difference in Two Means, Independent Samples Difference in Two Means, Matched Samples Proportions 273 *86 The Bootstrap 277 Chapter 8 Summary Hypothesis Testing Hypothesis Testing Using Confidence Intervals pvalue (OneSided) Classical Hypothesis Tests 300 *94 Classical Tests Reconsidered 306 *95 Operating Characteristics Curve (OCC) 310 *96 TwoSided Tests 314 Chapter 9 Summary Analysis of Variance (ANOVA) OneWay ANOVA TwoWay ANOVA Confidence Intervals 343 Chapter 10 Summary 346 Review Problems (Chapters 610) Confidence Intervals and Tests for p Predicting Vat a Given Level of X Extending the Model 389 Chapter 12 Summary Multiple Regression Why Multiple Regression? The Regression Model and Its OLS Fit Confidence Intervals and Statistical Tests Regression Coefficients as Multiplication Factors 410 *135 Simple and Multiple Regression Compared 417 *136 Path Analysis 424 Chapter 13 Summary Regression Extensions Dummy (01) Variables Analysis of Variance (ANOVA) by Regression Simplest Nonlinear Regression 449 *144 Nonlinearity Removed by Logs 452 *145 Diagnosis by Residual Plots 461 Chapter 14 Summary Correlation 474 PART III REGRESSION: RELATING TWO OR MORE VARIABLES Fitting a Line Introduction Ordinary Least Squares (OLS) Advantages of OLS and WLS 366 Chapter 11 Summary Simple Correlation Correlation and Regression 153 The Two Regression Lines Correlation in Multiple Regression Multicollinearity 501 Chapter 15 Summary 506 Review Problems (Chapters 1115) Simple Regression The Regression Model Sampling Variability 375 PART IV TOPICS IN CLASSICAL AND BAYESIAN INFERENCE 515
4 CONTENTS XV 16 Nonparametric and Robust Statistics (Requires Chapter 9) Introduction: Mean or Median? Sign Test for the Median Confidence Interval for the Median Wilcoxon Rank Test Rank Tests in General Runs Test for Independence Robust Statistics: Trimming and Weighting 536 Chapter 16 Summary ChiSquare Tests (Requires Chapter 9) x 2 Tests for Multinomials: Goodness of Fit x 2 Tests for Independence: Contingency Tables 555 Chapter 17 Summary 561 *18 Maximum Likelihood Estimation (Requires Chapter 7) Introduction MLE for Some Familiar Cases MLE for the Uniform Distribution MLE in General 576 Chapter 18 Summary 579 *19 Bayesian Inference (Requires Chapter 8) Posterior Distributions The Population Proportion The Mean ^ in a Normal Model The Slope /3 in Normal Regression Bayesian Shrinkage Estimates Classical and Bayesian Estimates Compared 615 Chapter 19 Summary 615 *20 Bayesian Decision Theory (Requires Chapter 19) 201 Maximizing Gain (or Minimizing Loss) Point Estimation as a Decision Classical and Bayesian Statistics Compared 633 Chapter 20 Summary 635 * Appendixes 22 Careful Approximation of the Median 638 N 25 Effects of a Liriear Transformation* Proofs Probability as Axiomatic Mathematics Easier Formula for o 2 : Proof Binomial Formula: Proof Calculus for Continuous Distributions Independent Implies Uncorrelated: Proof Linear Combinations: Proofs Central Limit Theorem Continuity Correction: Graphical Explanation 643 ( 72 Standard Error of X Consistency: Careful Definition Standard Error of (X,  X 2 ): Proof Confidence Interval for w. Derivation of Graph A More Exact pvalue for Proportions Breakdown of Total SS: Proof TwoWay ANOVA, Breakdown of Total SS: Proof ANOVA Is Much More Than Just Testing H o Lines and Planes LeastSquares Formulas: Proofs The Moments of fa: Proofs and Discussion A Onesided or Twosided Test?
5 XVI CONTENTS 124 Confidence Intervals above X o : Proofs Solution of a Set of Simultaneous Equations Direct Plus Indirect Relation: Proof Log Regression Handles a Multiplicative Error Term Correlation in Chapter 15 Agrees with Chapter ANOVA and r 2 : Proofs MLE for Some Familiar Cases: Proofs Bayesian Confidence Interval fortr Proof Posterior Distribution of fi in a Normal Model: Proof Posterior Distribution of j8 in Normal Regression: Proof Bayesian Shrinkage Confidence Intervals 662 Tables 663 References 679 Answers to OddNumbered Problems 685 Glossary of Common Symbols 699 Index of Examples and Problems 703 index 705
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
More informationMTH 140 Statistics Videos
MTH 140 Statistics Videos Chapter 1 Picturing Distributions with Graphs Individuals and Variables Categorical Variables: Pie Charts and Bar Graphs Categorical Variables: Pie Charts and Bar Graphs Quantitative
More informationInternational College of Economics and Finance Syllabus Probability Theory and Introductory Statistics
International College of Economics and Finance Syllabus Probability Theory and Introductory Statistics Lecturer: Mikhail Zhitlukhin. 1. Course description Probability Theory and Introductory Statistics
More informationCurriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 20092010
Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 20092010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different
More informationBusiness Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.
Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGrawHill/Irwin, 2008, ISBN: 9780073319889. Required Computing
More informationDescription. Textbook. Grading. Objective
EC151.02 Statistics for Business and Economics (MWF 8:008:50) Instructor: Chiu Yu Ko Office: 462D, 21 Campenalla Way Phone: 26093 Email: kocb@bc.edu Office Hours: by appointment Description This course
More informationStatistics Graduate Courses
Statistics Graduate Courses STAT 7002Topics in StatisticsBiological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.
More informationFairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
More informationCourse Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics
Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGrawHill/Irwin, 2010, ISBN: 9780077384470 [This
More informationbusiness statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
More informationService courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.
Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are
More informationLAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre
More informationSTA201TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance
Principles of Statistics STA201TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis
More informationIntroduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
More informationMATHEMATICAL METHODS OF STATISTICS
MATHEMATICAL METHODS OF STATISTICS By HARALD CRAMER TROFESSOK IN THE UNIVERSITY OF STOCKHOLM Princeton PRINCETON UNIVERSITY PRESS 1946 TABLE OF CONTENTS. First Part. MATHEMATICAL INTRODUCTION. CHAPTERS
More informationBusiness Analytics. Methods, Models, and Decisions. James R. Evans : University of Cincinnati PEARSON
Business Analytics Methods, Models, and Decisions James R. Evans : University of Cincinnati PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London
More informationStatistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013
Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.11.6) Objectives
More informationCHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS
Examples: Regression And Path Analysis CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS Regression analysis with univariate or multivariate dependent variables is a standard procedure for modeling relationships
More informationMATH BOOK OF PROBLEMS SERIES. New from Pearson Custom Publishing!
MATH BOOK OF PROBLEMS SERIES New from Pearson Custom Publishing! The Math Book of Problems Series is a database of math problems for the following courses: Prealgebra Algebra Precalculus Calculus Statistics
More information430 Statistics and Financial Mathematics for Business
Prescription: 430 Statistics and Financial Mathematics for Business Elective prescription Level 4 Credit 20 Version 2 Aim Students will be able to summarise, analyse, interpret and present data, make predictions
More informationSTAT 360 Probability and Statistics. Fall 2012
STAT 360 Probability and Statistics Fall 2012 1) General information: Crosslisted course offered as STAT 360, MATH 360 Semester: Fall 2012, Aug 20Dec 07 Course name: Probability and Statistics Number
More informationA spreadsheet Approach to Business Quantitative Methods
A spreadsheet Approach to Business Quantitative Methods by John Flaherty Ric Lombardo Paul Morgan Basil desilva David Wilson with contributions by: William McCluskey Richard Borst Lloyd Williams Hugh Williams
More information2013 MBA Jump Start Program. Statistics Module Part 3
2013 MBA Jump Start Program Module 1: Statistics Thomas Gilbert Part 3 Statistics Module Part 3 Hypothesis Testing (Inference) Regressions 2 1 Making an Investment Decision A researcher in your firm just
More informationTHE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK
THE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK SECOND EDITION T. M. Kubiak Donald W. Benbow ASQ Quality Press Milwaukee, Wisconsin Table of Contents list of Figures and Tables Preface to the Second Edition
More informationSimple Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
More informationII. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
More informationEmpirical ModelBuilding and Response Surfaces
Empirical ModelBuilding and Response Surfaces GEORGE E. P. BOX NORMAN R. DRAPER Technische Universitat Darmstadt FACHBEREICH INFORMATIK BIBLIOTHEK InvortarNf.. Sachgsbiete: Standort: New York John Wiley
More informationMultivariate Normal Distribution
Multivariate Normal Distribution Lecture 4 July 21, 2011 Advanced Multivariate Statistical Methods ICPSR Summer Session #2 Lecture #47/21/2011 Slide 1 of 41 Last Time Matrices and vectors Eigenvalues
More informationPROBABILITY AND STATISTICS. Ma 527. 1. To teach a knowledge of combinatorial reasoning.
PROBABILITY AND STATISTICS Ma 527 Course Description Prefaced by a study of the foundations of probability and statistics, this course is an extension of the elements of probability and statistics introduced
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationLinear regression methods for large n and streaming data
Linear regression methods for large n and streaming data Large n and small or moderate p is a fairly simple problem. The sufficient statistic for β in OLS (and ridge) is: The concept of sufficiency is
More informationDATA INTERPRETATION AND STATISTICS
PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE
More informationNEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS
NEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS TEST DESIGN AND FRAMEWORK September 2014 Authorized for Distribution by the New York State Education Department This test design and framework document
More informationOverview Classes. 123 Logistic regression (5) 193 Building and applying logistic regression (6) 263 Generalizations of logistic regression (7)
Overview Classes 123 Logistic regression (5) 193 Building and applying logistic regression (6) 263 Generalizations of logistic regression (7) 24 Loglinear models (8) 54 1517 hrs; 5B02 Building and
More informationGraduate Programs in Statistics
Graduate Programs in Statistics Course Titles STAT 100 CALCULUS AND MATR IX ALGEBRA FOR STATISTICS. Differential and integral calculus; infinite series; matrix algebra STAT 195 INTRODUCTION TO MATHEMATICAL
More informationE3: PROBABILITY AND STATISTICS lecture notes
E3: PROBABILITY AND STATISTICS lecture notes 2 Contents 1 PROBABILITY THEORY 7 1.1 Experiments and random events............................ 7 1.2 Certain event. Impossible event............................
More informationFrom the help desk: Bootstrapped standard errors
The Stata Journal (2003) 3, Number 1, pp. 71 80 From the help desk: Bootstrapped standard errors Weihua Guan Stata Corporation Abstract. Bootstrapping is a nonparametric approach for evaluating the distribution
More informationList of Examples. Examples 319
Examples 319 List of Examples DiMaggio and Mantle. 6 Weed seeds. 6, 23, 37, 38 Vole reproduction. 7, 24, 37 Wooly bear caterpillar cocoons. 7 Homophone confusion and Alzheimer s disease. 8 Gear tooth strength.
More informationRegression Modeling Strategies
Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions
More informationDirections for using SPSS
Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationI ~ 14J... <r ku...6l &J&!J=O
City College of San Francisco TechnologyMediated Course Proposal Course Outline Addendum I. GENERAL DESCRIPTION A. Date B. Department C. Course Identifier D. Course Title E. Addendum Preparer F. Chairperson
More informationOverview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
More informationEconomic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007)
COURSE DESCRIPTION Title Code Level Semester Credits 3 Prerequisites Post requisites Introduction to Statistics ECON1005 (EC160) I I None Economic Statistics (ECON2006), Statistics and Research Design
More informationMultivariate Statistical Inference and Applications
Multivariate Statistical Inference and Applications ALVIN C. RENCHER Department of Statistics Brigham Young University A WileyInterscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim
More informationT O P I C 1 2 Techniques and tools for data analysis Preview Introduction In chapter 3 of Statistics In A Day different combinations of numbers and types of variables are presented. We go through these
More informationApplied Regression Analysis and Other Multivariable Methods
THIRD EDITION Applied Regression Analysis and Other Multivariable Methods David G. Kleinbaum Emory University Lawrence L. Kupper University of North Carolina, Chapel Hill Keith E. Muller University of
More informationUNIT 1: COLLECTING DATA
Core Probability and Statistics Probability and Statistics provides a curriculum focused on understanding key data analysis and probabilistic concepts, calculations, and relevance to realworld applications.
More informationSPSS Tests for Versions 9 to 13
SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationTtest & factor analysis
Parametric tests Ttest & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue
More informationQuantitative Methods for Finance
Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain
More informationMULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance
More informationTeaching Business Statistics through Problem Solving
Teaching Business Statistics through Problem Solving David M. Levine, Baruch College, CUNY with David F. Stephan, Two Bridges Instructional Technology CONTACT: davidlevine@davidlevinestatistics.com Typical
More information**BEGINNING OF EXAMINATION** The annual number of claims for an insured has probability function: , 0 < q < 1.
**BEGINNING OF EXAMINATION** 1. You are given: (i) The annual number of claims for an insured has probability function: 3 p x q q x x ( ) = ( 1 ) 3 x, x = 0,1,, 3 (ii) The prior density is π ( q) = q,
More informationNEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York
NEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York DEPARTMENT: Mathematics COURSE: MAT 1272/ MA 272 TITLE: DESCRIPTION: TEXT: Statistics An introduction to statistical methods and statistical
More informationPS 271B: Quantitative Methods II. Lecture Notes
PS 271B: Quantitative Methods II Lecture Notes Langche Zeng zeng@ucsd.edu The Empirical Research Process; Fundamental Methodological Issues 2 Theory; Data; Models/model selection; Estimation; Inference.
More informationModule 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
More informationSilvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spsssa.com
SPSSSA Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spsssa.com SPSSSA Training Brochure 2009 TABLE OF CONTENTS 1 SPSS TRAINING COURSES FOCUSING
More informationPELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050
PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050 Class Hours: 2.0 Credit Hours: 3.0 Laboratory Hours: 2.0 Date Revised: Fall 2013 Catalog Course Description: Descriptive
More informationSimulation Exercises to Reinforce the Foundations of Statistical Thinking in Online Classes
Simulation Exercises to Reinforce the Foundations of Statistical Thinking in Online Classes Simcha Pollack, Ph.D. St. John s University Tobin College of Business Queens, NY, 11439 pollacks@stjohns.edu
More informationQUANTITATIVE METHODS. for Decision Makers. Mik Wisniewski. Fifth Edition. FT Prentice Hall
Fifth Edition QUANTITATIVE METHODS for Decision Makers Mik Wisniewski Senior Research Fellow, Department of Management Science, University of Strathclyde Business School FT Prentice Hall FINANCIAL TIMES
More informationAdequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection
Directions in Statistical Methodology for Multivariable Predictive Modeling Frank E Harrell Jr University of Virginia Seattle WA 19May98 Overview of Modeling Process Model selection Regression shape Diagnostics
More informationPractical. I conometrics. data collection, analysis, and application. Christiana E. Hilmer. Michael J. Hilmer San Diego State University
Practical I conometrics data collection, analysis, and application Christiana E. Hilmer Michael J. Hilmer San Diego State University Mi Table of Contents PART ONE THE BASICS 1 Chapter 1 An Introduction
More informationStatistical Models in R
Statistical Models in R Some Examples Steven Buechler Department of Mathematics 276B Hurley Hall; 16233 Fall, 2007 Outline Statistical Models Structure of models in R Model Assessment (Part IA) Anova
More informationUniversally Accepted Lean Six Sigma Body of Knowledge for Green Belts
Universally Accepted Lean Six Sigma Body of Knowledge for Green Belts The IASSC Certified Green Belt Exam was developed and constructed based on the topics within the body of knowledge listed here. Questions
More informationBasic Statistics and Data Analysis for Health Researchers from Foreign Countries
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association
More information1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number
1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x  x) B. x 3 x C. 3x  x D. x  3x 2) Write the following as an algebraic expression
More informationMethods for Metaanalysis in Medical Research
Methods for Metaanalysis in Medical Research Alex J. Sutton University of Leicester, UK Keith R. Abrams University of Leicester, UK David R. Jones University of Leicester, UK Trevor A. Sheldon University
More informationAP Statistics: Syllabus 1
AP Statistics: Syllabus 1 Scoring Components SC1 The course provides instruction in exploring data. 4 SC2 The course provides instruction in sampling. 5 SC3 The course provides instruction in experimentation.
More informationConfidence Intervals for Spearman s Rank Correlation
Chapter 808 Confidence Intervals for Spearman s Rank Correlation Introduction This routine calculates the sample size needed to obtain a specified width of Spearman s rank correlation coefficient confidence
More informationSection Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini
NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building
More informationACTL5101 Probability and Statistics for Actuaries
Australian School of Business School of Risk and Actuarial Studies ACTL5101 Probability and Statistics for Actuaries Course Outline Semester 1, 2014 Part A: CourseSpecific Information Please consult Part
More informationStatistics courses often teach the twosample ttest, linear regression, and analysis of variance
2 Making Connections: The TwoSample ttest, Regression, and ANOVA In theory, there s no difference between theory and practice. In practice, there is. Yogi Berra 1 Statistics courses often teach the twosample
More informationAPPLIED MATHEMATICS ADVANCED LEVEL
APPLIED MATHEMATICS ADVANCED LEVEL INTRODUCTION This syllabus serves to examine candidates knowledge and skills in introductory mathematical and statistical methods, and their applications. For applications
More informationCHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA
Examples: Multilevel Modeling With Complex Survey Data CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA Complex survey data refers to data obtained by stratification, cluster sampling and/or
More informationIntroduction to Multilevel Modeling Using HLM 6. By ATS Statistical Consulting Group
Introduction to Multilevel Modeling Using HLM 6 By ATS Statistical Consulting Group Multilevel data structure Students nested within schools Children nested within families Respondents nested within interviewers
More informationModule 3: Correlation and Covariance
Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis
More informationElements of statistics (MATH04871)
Elements of statistics (MATH04871) Prof. Dr. Dr. K. Van Steen University of Liège, Belgium December 10, 2012 Introduction to Statistics Basic Probability Revisited Sampling Exploratory Data Analysis 
More informationPCHS ALGEBRA PLACEMENT TEST
MATHEMATICS Students must pass all math courses with a C or better to advance to the next math level. Only classes passed with a C or better will count towards meeting college entrance requirements. If
More informationCHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
More informationCONTENTS OF DAY 2. II. Why Random Sampling is Important 9 A myth, an urban legend, and the real reason NOTES FOR SUMMER STATISTICS INSTITUTE COURSE
1 2 CONTENTS OF DAY 2 I. More Precise Definition of Simple Random Sample 3 Connection with independent random variables 3 Problems with small populations 8 II. Why Random Sampling is Important 9 A myth,
More informationProbability and Statistics
Probability and Statistics Syllabus for the TEMPUS SEE PhD Course (Podgorica, April 4 29, 2011) Franz Kappel 1 Institute for Mathematics and Scientific Computing University of Graz Žaneta Popeska 2 Faculty
More informationExample: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.
Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation:  Feature vector X,  qualitative response Y, taking values in C
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 SigmaRestricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More informationSENSITIVITY ANALYSIS AND INFERENCE. Lecture 12
This work is licensed under a Creative Commons AttributionNonCommercialShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationSAS Certificate Applied Statistics and SAS Programming
SAS Certificate Applied Statistics and SAS Programming SAS Certificate Applied Statistics and Advanced SAS Programming Brigham Young University Department of Statistics offers an Applied Statistics and
More informationSAS Software to Fit the Generalized Linear Model
SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling
More informationAuxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
More informationCome scegliere un test statistico
Come scegliere un test statistico Estratto dal Capitolo 37 of Intuitive Biostatistics (ISBN 0195086074) by Harvey Motulsky. Copyright 1995 by Oxfd University Press Inc. (disponibile in Iinternet) Table
More informationStatistical Rules of Thumb
Statistical Rules of Thumb Second Edition Gerald van Belle University of Washington Department of Biostatistics and Department of Environmental and Occupational Health Sciences Seattle, WA WILEY AJOHN
More informationStatistics for Experimenters
Statistics for Experimenters Design, Innovation, and Discovery Second Edition GEORGE E. P. BOX J. STUART HUNTER WILLIAM G. HUNTER WILEY INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION FACHGEBIETSBGCHEREI
More informationCHAPTER 8 EXAMPLES: MIXTURE MODELING WITH LONGITUDINAL DATA
Examples: Mixture Modeling With Longitudinal Data CHAPTER 8 EXAMPLES: MIXTURE MODELING WITH LONGITUDINAL DATA Mixture modeling refers to modeling with categorical latent variables that represent subpopulations
More informationNCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
More informationChapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 3 Student Lecture Notes 3 Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
More informationRegression Analysis: A Complete Example
Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty
More informationROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE. School of Mathematical Sciences
! ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences New Revised COURSE: COSMATH252 Probability and Statistics II 1.0 Course designations and approvals:
More informationEconometrics and Data Analysis I
Econometrics and Data Analysis I Yale University ECON S131 (ONLINE) Summer Session A, 2014 June 2 July 4 Instructor: Doug McKee (douglas.mckee@yale.edu) Teaching Fellow: Yu Liu (dav.yu.liu@yale.edu) Classroom:
More informationPenalized regression: Introduction
Penalized regression: Introduction Patrick Breheny August 30 Patrick Breheny BST 764: Applied Statistical Modeling 1/19 Maximum likelihood Much of 20thcentury statistics dealt with maximum likelihood
More information